Self-Attending Task Generative Adversarial Network for Realistic Satellite Image Creation

• URL: http://arxiv.org/abs/2111.09463v1
• Date: Thu, 18 Nov 2021 00:49:18 GMT
• Title: Self-Attending Task Generative Adversarial Network for Realistic Satellite Image Creation
• Abstract summary: We introduce a self-attending task generative adversarial network (SATGAN) to augment synthetic high contrast scientific imagery of resident space objects. The architecture is shown to reduce instances of hallucinatory objects or obfuscation of semantic content in context images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a self-attending task generative adversarial network (SATGAN) and apply it to the problem of augmenting synthetic high contrast scientific imagery of resident space objects with realistic noise patterns and sensor characteristics learned from collected data. Augmenting these synthetic data is challenging due to the highly localized nature of semantic content in the data that must be preserved. Real collected images are used to train a network what a given class of sensor's images should look like. The trained network then acts as a filter on noiseless context images and outputs realistic-looking fakes with semantic content unaltered. The architecture is inspired by conditional GANs but is modified to include a task network that preserves semantic information through augmentation. Additionally, the architecture is shown to reduce instances of hallucinatory objects or obfuscation of semantic content in context images representing space observation scenes.

Related papers

• VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systems [0.0]
  We present VERITAS, a comprehensive framework that accurately detects whether a small (32x32) image is AI-generated.<n>VERITAS produces human-readable explanations that describe key artifacts in synthetic images.
  arXiv  Detail & Related papers  (2025-07-07T15:57:05Z)
• StableSemantics: A Synthetic Language-Vision Dataset of Semantic Representations in Naturalistic Images [5.529078451095096]
  understanding the semantics of visual scenes is a fundamental challenge in Computer Vision. Recent advancements in text-to-image frameworks have led to models that implicitly capture natural scene statistics. Our work presents StableSemantics, a dataset comprising 224 thousand human-curated prompts, processed natural language captions, over 2 million synthetic images, and 10 million attention maps corresponding to individual noun chunks.
  arXiv  Detail & Related papers  (2024-06-19T17:59:40Z)
• Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization [62.157627519792946]
  We introduce a novel framework called bridged transfer, which initially employs synthetic images for fine-tuning a pre-trained model to improve its transferability. We propose dataset style inversion strategy to improve the stylistic alignment between synthetic and real images. Our proposed methods are evaluated across 10 different datasets and 5 distinct models, demonstrating consistent improvements.
  arXiv  Detail & Related papers  (2024-03-28T22:25:05Z)
• Unlocking Pre-trained Image Backbones for Semantic Image Synthesis [29.688029979801577]
  We propose a new class of GAN discriminators for semantic image synthesis that generates highly realistic images. Our model, which we dub DP-SIMS, achieves state-of-the-art results in terms of image quality and consistency with the input label maps on ADE-20K, COCO-Stuff, and Cityscapes.
  arXiv  Detail & Related papers  (2023-12-20T09:39:19Z)
• Dual Pyramid Generative Adversarial Networks for Semantic Image Synthesis [94.76988562653845]
  The goal of semantic image synthesis is to generate photo-realistic images from semantic label maps. Current state-of-the-art approaches, however, still struggle to generate realistic objects in images at various scales. We propose a Dual Pyramid Generative Adversarial Network (DP-GAN) that learns the conditioning of spatially-adaptive normalization blocks at all scales jointly.
  arXiv  Detail & Related papers  (2022-10-08T18:45:44Z)
• Panoptic Segmentation using Synthetic and Real Data [21.226582774172062]
  We propose a pipeline which allows to generate synthetic images from 3D models of real environments and real objects. Experiments show that the use of synthetic images allows to drastically reduce the number of real images needed to obtain reasonable panoptic segmentation performance.
  arXiv  Detail & Related papers  (2022-04-14T16:01:48Z)
• A Shared Representation for Photorealistic Driving Simulators [83.5985178314263]
  We propose to improve the quality of generated images by rethinking the discriminator architecture. The focus is on the class of problems where images are generated given semantic inputs, such as scene segmentation maps or human body poses. We aim to learn a shared latent representation that encodes enough information to jointly do semantic segmentation, content reconstruction, along with a coarse-to-fine grained adversarial reasoning.
  arXiv  Detail & Related papers  (2021-12-09T18:59:21Z)
• Semantic-Aware Generation for Self-Supervised Visual Representation Learning [116.5814634936371]
  We advocate for Semantic-aware Generation (SaGe) to facilitate richer semantics rather than details to be preserved in the generated image. SaGe complements the target network with view-specific features and thus alleviates the semantic degradation brought by intensive data augmentations. We execute SaGe on ImageNet-1K and evaluate the pre-trained models on five downstream tasks including nearest neighbor test, linear classification, and fine-scaled image recognition.
  arXiv  Detail & Related papers  (2021-11-25T16:46:13Z)
• Dynamic Object Removal and Spatio-Temporal RGB-D Inpainting via Geometry-Aware Adversarial Learning [9.150245363036165]
  Dynamic objects have a significant impact on the robot's perception of the environment. In this work, we address this problem by synthesizing plausible color, texture and geometry in regions occluded by dynamic objects. We optimize our architecture using adversarial training to synthesize fine realistic textures which enables it to hallucinate color and depth structure in occluded regions online.
  arXiv  Detail & Related papers  (2020-08-12T01:23:21Z)
• Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement [78.58603635621591]
  Training an unpaired synthetic-to-real translation network in image space is severely under-constrained. We propose a semi-supervised approach that operates on the disentangled shading and albedo layers of the image. Our two-stage pipeline first learns to predict accurate shading in a supervised fashion using physically-based renderings as targets.
  arXiv  Detail & Related papers  (2020-03-27T21:45:41Z)
• Scene Text Synthesis for Efficient and Effective Deep Network Training [62.631176120557136]
  We develop an innovative image synthesis technique that composes annotated training images by embedding foreground objects of interest into background images. The proposed technique consists of two key components that in principle boost the usefulness of the synthesized images in deep network training. Experiments over a number of public datasets demonstrate the effectiveness of our proposed image synthesis technique.
  arXiv  Detail & Related papers  (2019-01-26T10:15:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.